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Most CLO-002 Reliable Questions & CLO-002 Valid Exam Topics

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CompTIA CLO-002 exam covers various topics related to cloud computing, including cloud computing concepts, cloud infrastructure, virtualization, and cloud security. CLO-002 exam also covers the business aspects of cloud computing, such as cloud service management, cloud migration, and cloud economics. CLO-002 Exam is designed to test the candidate’s understanding of cloud computing concepts, best practices, and technologies.

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CompTIA CLO-002 (CompTIA Cloud Essentials+ Certification) Certification Exam is a widely recognized credential that validates the knowledge and skills required for IT professionals to understand the fundamentals of cloud computing. It validates a candidate's knowledge of cloud computing concepts, models, services, and technologies, as well as their ability to evaluate cloud computing solutions and their impact on the business. CompTIA Cloud Essentials+ Certification Exam certification is ideal for IT professionals who are looking to enhance their skills and knowledge in cloud computing and take their career to the next level.

CompTIA Cloud Essentials+ Certification Exam Sample Questions (Q215-Q220):

NEW QUESTION # 215
A cloud administrator needs to enable users to access business applications remotely while ensuring these applications are only installed on company-controlled equipment. All users require the ability to modify personal working environments. Which of the following is the BEST solution?

A. SSO
B. VDI
C. SSH
D. VPN

Answer: D

Explanation:
Explanation
Explanation/Reference:

NEW QUESTION # 216
A company is required to move its human resources application to the cloud to reduce capital expenses. The
IT team does a feasibility analysis and learns the application requires legacy infrastructure and cannot be
moved to the cloud.
Which of the following is the MOST appropriate cloud migration approach for the company?

A. Hybrid
B. Rip and replace
C. In-place upgrade
D. Lift and shift

Answer: A

NEW QUESTION # 217
Which of the following describes the process of moving an application from an isolated data center to reduce latency and ensure close proximity to end users?

A. Backup
B. Geo-redundancy
C. Replication
D. Zones

Answer: B

Explanation:
Geo-redundancy is the distribution of mission-critical components or infrastructures, such as servers, across multiple data centers that reside in different geographic locations1. Geo-redundancy acts as a safety net in case the primary site fails or in the event of a disaster or an outage that impacts an entire region1. Geo-redundancy also reduces latency and ensures close proximity to end users by delivering web content from the nearest data center2. Geo-redundancy is a common feature of cloud computing, as it provides high availability, reliability, and performance for cloud applications and services2.
Replication is the process of copying data from one location to another, such as from a primary site to a secondary site, or from one cloud provider to another3. Replication is a necessary but not sufficient condition for geo-redundancy, as it does not guarantee that the replicated data is accessible or consistent across different regions3. Replication can also introduce operational complexity and data synchronization issues3.
Zones are logical or physical partitions of a cloud provider's infrastructure that offer high availability and fault tolerance within a region4. Zones are usually located in the same or nearby data centers, and are connected by low-latency network links4. Zones can help distribute the workload and prevent single points of failure, but they do not provide geo-redundancy, as they are still vulnerable to regional outages or disasters4.
Backup is the process of creating and storing copies of data for the purpose of recovery in case of data loss or corruption5. Backup is an important part of data protection and disaster recovery, but it does not provide geo-redundancy, as it does not ensure that the backup data is available or up-to-date in different regions5. Backup can also have longer recovery time and higher cost than geo-redundancy5. Reference: Use geo-redundancy to design highly available applications; Geo Redundancy Explained | Cloudify; Georedundancy - Open Telekom Cloud; Why geo-redundancy for cloud infrastructure is a 'must have'; Geo-Redundancy: Why Is It So Important? | Unitrends.

NEW QUESTION # 218
Which of the following would be expected from a security consultant who has been hired to investigate a data breach of a private cloud instance?

A. Incident report
B. Application scan results
C. Request for information
D. Risk register

Answer: A

NEW QUESTION # 219
Which of the following are true about the use of machine learning in a cloud environment? (Choose two).

A. Just one type of cloud storage is available in the cloud for machine learning workloads.
B. Specialized machine learning algorithms can be deployed to optimize results for specific scenarios.
C. Using machine learning solutions in the cloud removes the data-gathering step from the learning process.
D. Machine learning can leverage processes in a cloud environment through the use of cloud storage and auto-scaling.
E. Machine learning requires a specialized IT team to create the machine learning models from scratch.
F. Machine learning can just be hosted in the cloud for managed services.

Answer: B,D

Explanation:
Machine learning is a subset of artificial intelligence that enables a system to autonomously learn and improve using neural networks and deep learning, without being explicitly programmed, by feeding it large amounts of data1. Machine learning can be used in a cloud environment to leverage the benefits of cloud computing, such as scalability, flexibility, and cost-effectiveness. Some of the ways that machine learning can use cloud processes are:
Specialized machine learning algorithms can be deployed to optimize results for specific scenarios. Depending on the use case, an organization may choose different cloud services to support their machine learning projects, such as artificial intelligence as a service (AIaaS) or GPU as a service (GPUaaS)2. AIaaS provides pre-trained models for common tasks, such as image recognition, natural language processing, or sentiment analysis, while GPUaaS provides access to high-performance computing resources for training custom models. These services can help organizations achieve better results faster and more efficiently.
Machine learning can leverage processes in a cloud environment through the use of cloud storage and auto-scaling. Cloud storage provides a scalable and secure way to store and access large amounts of data, which is essential for machine learning. Cloud storage also enables data integration and collaboration across different sources and platforms3. Auto-scaling is a feature of cloud computing that automatically adjusts the amount of resources allocated to a machine learning application based on the demand and workload. This helps optimize the performance and cost of machine learning in the cloud4.
The other options are false because:
Machine learning can just be hosted in the cloud for managed services. This is not true because machine learning can also be used in a hybrid or multi-cloud environment, where some components of the machine learning project are hosted on-premises or on different cloud providers. This can provide more flexibility and control over the machine learning process, as well as address security and compliance issues2.
Just one type of cloud storage is available in the cloud for machine learning workloads. This is not true because there are different types of cloud storage available for machine learning workloads, such as object storage, block storage, or file storage. Each type of storage has its own advantages and disadvantages, depending on the data format, size, and access frequency. For example, object storage is suitable for storing unstructured data, such as images or videos, while block storage is suitable for storing structured data, such as databases or files3.
Machine learning requires a specialized IT team to create the machine learning models from scratch. This is not true because machine learning does not always require a specialized IT team to create the models from scratch. There are many tools and services available in the cloud that can help simplify and automate the machine learning process, such as data preparation, model building, testing, deployment, and monitoring. For example, Google Cloud AutoML is a service that allows users to create custom machine learning models with minimal coding and expertise4.
Using machine learning solutions in the cloud removes the data-gathering step from the learning process. This is not true because using machine learning solutions in the cloud does not remove the data-gathering step from the learning process. Data-gathering is a crucial step in machine learning, as it provides the input for the machine learning models to learn from. Data-gathering involves collecting, cleaning, labeling, and transforming data from various sources, such as sensors, databases, or web pages. Using machine learning solutions in the cloud can help with data-gathering, but it does not eliminate it3.
Reference:
1: What is Machine Learning? Types & Uses | Google Cloud
2: Machine Learning in the Cloud: Complete Guide [2023] - Run
3: Role: Artificial Intelligence & Machine Learning in Cloud Environment
4: Data science and machine learning on Cloud AI Platform

NEW QUESTION # 220
......

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